Learn Machine Learning 101 Class Bootcamp Course NYC

Machine Learning 101 Class Bootcamp Course NYC

1. Python Scikit-learn Library
2. Supervised vs Unsupervised Learning
3. Regression vs Classification models
4. Categorical vs Continuous feature spaces
5. Modeling Fundamentals: Test-train split, Cross validation(CV), Bias–variance tradeoff, Precision and Recall, Ensemble models
6. Interpreting Results of Regression and  Classification Models (Hands On)
7. Parameters and Hyper Parameters
8. SVM, K-Nearest Neighbor, Neural Networks
9. Dimension Reduction

Hands on:

1. Understanding and Interpreting results of Regression and Logistic Regression using Google Spreadsheets and Python
2. Calculating R-Square, MSE, Logit manually in excel for enhanced understanding (Multiple Regression)
3. Understanding features of Popular Datasets: Titanic, Iris (Scikit) and Housing Prices
4. Running Logistic Regression on Titanic Data Set
5. Running Regression, Logistic Regression, SVM and Random Forest on Iris Dataset